35 lines
1.1 KiB
Python
35 lines
1.1 KiB
Python
# Importing the libraries
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
import pandas as pd
|
|
|
|
# Importing the dataset
|
|
dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')
|
|
X = dataset.iloc[:, [2, 3]].values
|
|
y = dataset.iloc[:, 4].values
|
|
|
|
# Splitting the dataset into the Training set and Test set
|
|
from sklearn.model_selection import train_test_split
|
|
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
|
|
|
|
# Feature Scaling
|
|
from sklearn.preprocessing import StandardScaler
|
|
sc = StandardScaler()
|
|
X_train = sc.fit_transform(X_train)
|
|
X_test = sc.transform(X_test)
|
|
|
|
# Fitting K-NN to the Training set
|
|
from sklearn.neighbors import KNeighborsClassifier
|
|
classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
|
|
classifier.fit(X_train, y_train)
|
|
|
|
# Predicting the Test set results
|
|
y_pred = classifier.predict(X_test)
|
|
|
|
# Making the Confusion Matrix
|
|
from sklearn.metrics import confusion_matrix
|
|
from sklearn.metrics import classification_report
|
|
cm = confusion_matrix(y_test, y_pred)
|
|
print(cm)
|
|
print(classification_report(y_test, y_pred))
|